Object data processing method and device, computer equipment and storage medium

Abstract

The invention relates to an object data processing method and device, computer equipment and a storage medium. The method relates to a decision tree model in the field of machine learning, and comprises the following steps: converting object behavior data corresponding to each portrait dimension into a strategy effect corresponding to a target strategy according to the proportion of a first hit category belonging to the implementation of the target strategy on a sample object in the object behavior data corresponding to each portrait dimension of the object portrait data; inputting the strategy effect corresponding to each portrait dimension into a decision tree model, and training the decision tree model by taking strategy effect optimization corresponding to portrait dimension combinations represented by nodes in the decision tree model as a target to obtain a group division decision tree; and determining an optimal subdivision group corresponding to the target strategy based on thestrategy effect corresponding to each subdivision group represented from the root node to the leaf node of the group division decision tree. By adopting the method, the efficiency of mining the subdivided group strategy result from the test result can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to an object data processing method, device, computer equipment and storage medium. Background technique [0002] A / B testing is to divide the tested objects into Group A and Group B, implement the target strategy for the objects in Group A, and not implement the target strategy for the objects in Group B, and analyze the object behavior data of the tested objects in Group A and Group B difference to determine the most effective strategy for optimizing the metric. [0003] With the development of computer technology and artificial intelligence technology, A / B testing has been widely used. For example, in the Internet product optimization scenario, the test results of the A / B test are used to determine whether to launch the optimization strategy. In the initial stage of Internet product launch, the optimization strategy can often be recognized by the overall tested obje...

AI Technical Summary

Problems solved by technology

Portrait dimensions include city, age, and gender, for example. City attribute values ​​include Beijing, Shanghai, Shenzhen, Guangzhou, etc., age attribute values ​​include 1-100 years old, and gender attribute values ​​include male and female. Due to the large number of image dimensions, Due to the large number of attribute values ​​in the portrait dimension and other factors, the combination of different attribute values ​​often encounters the problem of combination explosion, resulting in computing performance bottlenecks, which in turn leads to low efficiency in mining subdivided group test results from test results

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